With the advancement of science and technology, there is a higher and higher requirement on the quality of objects, even to a degree of “almost perfection”. Upon an object is subjected to a force, it will exhibit mechanical properties such as deformation and failure. It is one of important subjects in material science to make a material to be without exceeding its failure limit, while still having good stability and economy. Due to production defects or wear in use, various deformations, such as cracks, holes, road ruts, track bending, lining deformation, can deformation, pipe deformation, etc., may occur on a surface of an object, thereby affecting the performance of the object and even causing a safety accident. Therefore, a timely detection of surface deformation of objects has important value and significance in effectively preventing safety accidents, reducing economic losses, and improving product quality and the like.
At present, a vast majority of surface deformation detection methods of objects depend on the examination by human eyes, thus the detection result depends on subjectivity of a person. Meanwhile, as a person works for a long time, his or her eyes are prone to fatigue. At this time, false detection and miss detection rates are extremely high. Therefore, a detection method relying on human eyes cannot effectively detect surface deformation of an object, and at the same time waste a lot of resources of labor. In addition, there is an automated surface defect detection technology based on two-dimensional machine vision. This technology obtains two-dimensional profile information of surface defects of an object through brightness information reflected by the object, thereby realizing defect detection of the surface of the object. However, this detection method cannot obtain depth information on the defects of the object. Meanwhile, in many cases, as a notable two-dimensional feature of a defect cannot be obtained by utilizing a special light source, it becomes very difficult to identify the defect, thereby a result of identification is unsatisfactory as compared with that of the examination by human eyes, and a further study on the result of identification has to be made to meet requirements of detection in production.
At present, a three dimensional (3D) modeling technology has been widely used in a wide range of fields from macro land survey, three dimensional visualization, three dimensional animation, high precision three dimensional modeling to three dimensional printing. According to laser triangulation principle, a measurement method based on line structured light in combination with visual sensors can realize synchronous measurement in a same attitude and at a same time, that is, a complete section can be sampled through one measurement to ensure that the obtained data of the section is based on the same attitude. Three-dimensional information of a sectional profile of an object may be accurately obtained with high precision based on three-dimensional point cloud data measured based on line structured light in combination with visual sensors, and two-dimensional information of a defect may also be obtained at the same time, thereby complete information about deformation of the object, including a position of deformation, a degree of deformation, etc., can be obtained based on the three-dimensional point cloud data directly and conveniently.
Currently, there are two main types of automated surface defect detection technology:
(1) with a surface defect detection technology based on two-dimensional machine vision, imaging quality of a defect of a product is the key to affect an identification rate of defect, and a shape, orientation, surface material and texture of the defect all directly affect imaging quality. As the illumination of light can have impact on imaging of defects, different sources of light, illumination angles and intensities are used for different defects. However, it is difficult to propose a special algorithm with universality as there are so many kinds of defects, which proposes a big challenge for the identification of defects. Meanwhile, this kind of detection method cannot obtain depth information on defects of an object, that is, the degree of damage of defects cannot be accurately and effectively evaluated, and it is also impossible to detect whether a large range of deformation has occurred on the object.
(2) With an existing method for extracting deformation features of objects based on three dimensional laser radar technology, a rotating prism is used to measure a single section and a rotating gimbal is used to scan an entire field of view to acquire a three-dimensional point cloud of an object. Based on the time-of-flight differential pulse measurement, a measurement accuracy reaches millimeter level, and a measurement speed reaches more than one million points per second. The prism and the gimbal rotate synchronously during measurement, and a measurement section is a non-strict section (obtained not in a same time and in a space), that is, the three-dimensional point cloud of a surface of the object is composed of discrete points. This method can be used to monitor a fixed site where an object deforms slowly. However, in areas such as road defect detection, tunnel surveying, track defect detection, online chip micro defect detection and cultural relic archaeology, it is required to measure in a high-dynamic environment, and it is required to obtain a section in a strict sense at one measurement, that is, the points on the section are measured at a same attitude and at a same time. For example, for road rutting detection, it requires that a measurement width is at least 2000 mm or more, the measurement resolution (sampling interval for the points on the same section) reaches at least millimeter level, the distance measurement accuracy reaches at least 0.01 mm, and the measurement frequency is 10 KHz or more, i.e., 200 million points may be measured per second. Conventional three dimensional laser radar measurement techniques are all unable to meet such requirements of measurement.